Prenatal air pollution and children’s autism traits score: Examination of joint associations with maternal intake of vitamin D, methyl donors, and polyunsaturated fatty acids using mixture methods

Background: Maternal nutrient intake may moderate associations between environmental exposures and children’s neurodevelopmental outcomes, but few studies have assessed joint effects. We aimed to evaluate whether prenatal nutrient intake influences the association between air pollutants and autism-related trait scores. Methods: We included 126 participants from the EARLI (Early Autism Risk Longitudinal Investigation, 2009–2012) cohort, which followed US pregnant mothers who previously had a child with autism. Bayesian kernel machine regression and traditional regression models were used to examine joint associations of prenatal nutrient intake (vitamins D, B12, and B6; folate, choline, and betaine; and total omega 3 and 6 polyunsaturated fatty acids, reported via food frequency questionnaire), air pollutant exposure (particulate matter <2.5 μm [PM2.5], nitrogen dioxide [NO2], and ozone [O3], estimated at the address level), and children’s autism-related traits (measured by the Social Responsiveness Scale [SRS] at 36 months). Results: Most participants had nutrient intakes and air pollutant exposures that met US standards. Bayesian kernel machine regression mixture models and traditional regression models provided little evidence of individual or joint associations of nutrients and air pollutants with SRS scores or of an association between the overall mixture and SRS scores. Conclusion: In this cohort with a high familial likelihood of autism, we did not observe evidence of joint associations between air pollution exposures and nutrient intake with autism-related traits. Future work should examine the use of these methods in larger, more diverse samples, as our results may have been influenced by familial liability and/or relatively high nutrient intakes and low air pollutant exposures.

eTable 3. Posterior inclusion probabilities (PIPs) for the association of prenatal nutrient intakes and air pollutant exposures with SRS score in the EARLI study, from Bayesian kernel machine regression models (n = 126) eTable 4. Bivariate associations of prenatal nutrient intake and air pollution exposure with autism traits scores in the EARLI study (n = 126) eTable 5.The association of average prenatal exposure to air pollutants with autism diagnosis in the EARLI study, stratified by nutrient intake below vs above the median eFigure 1. Bayesian kernel machine regression (BKMR) results for the association of prenatal nutrient intake and air pollution exposures with child SRS total raw score in the EARLI study, with further adjustment for covariates (n = 126) eFigure 2. Bayesian kernel machine regression (BKMR) results for the association of prenatal nutrient intake and trimester-specific air pollution exposures with child SRS total raw score in the EARLI study (n = 126) eFigure 3. Bayesian kernel machine regression (BKMR) results for the association of prenatal nutrient intake in the second half of pregnancy and air pollution exposures with child SRS total raw score in the EARLI study (n = 79) eFigure 4. Probit Bayesian kernel machine regression (BKMR) results for the association of prenatal nutrient intake and air pollution exposures with child Autism diagnosis in the EARLI study (n = 144) Results are adjusted for child sex, birth year and season, gestational age at birth, whether the child was ever breastfed; maternal age, race and ethnicity, socioeconomic deprivation index, prenatal vitamin/supplement use in first month of pregnancy, interpregnancy interval between end of most recent pregnancy and the current, maternal antidepressant use during pregnancy, pre-pregnancy BMI, gestational weight gain, parity, and Alternative Healthy Eating Index score for pregnancy (a measure of diet quality) 45 , census tract-based socioeconomic status using the index of concentration at the extremes; 46 and study site.Air pollutant values are reverse coded.
Plot A shows the association of the overall mixture with SRS score, when all exposures are set at the same quantile, compared to when all exposures are at their 50 th percentile.Plot B shows the independent associations of each exposure with SRS, with all other exposures at their 50th percentile.Plot C shows the association of a single exposure with SRS score, when a second exposure is set at varying quantiles, illustrating potential bivariate interactive effects.Plot D shows the association of a single exposure with SRS score as it increases from its 25 th to 75 th percentile while all other exposures are set at specific quantiles.For all plots, covariates are held constant.Results are adjusted for study site, maternal age, maternal race and ethnicity, maternal socioeconomic deprivation index, and child sex.Air pollutant values are reverse coded.Plot A shows the association of the overall mixture with SRS score, when all exposures are set at the same quantile, compared to when all exposures are at their 50 th percentile.Plot B shows the independent associations of each exposure with SRS, with all other exposures at their 50th percentile.Plot C shows the association of a single exposure with SRS score, when a second exposure is set at varying quantiles, illustrating potential bivariate interactive effects.Plot D shows the association of a single exposure with SRS score as it increases from its 25 th to 75 th percentile while all other exposures are set at specific quantiles.For all plots, covariates are held constant.eFigure 3. Bayesian kernel machine regression (BKMR) results for the association of prenatal nutrient intake in the second half of pregnancy and air pollution exposures with child SRS total raw score in the EARLI study (n = 79) EARLI -Early Autism Risk Longitudinal Investigation; SRS -Social Responsiveness Scale Results are adjusted for study site, maternal age, maternal race and ethnicity, maternal socioeconomic deprivation index, and child sex.Air pollutant values are reverse coded.Plot A shows the association of the overall mixture with SRS score, when all exposures are set at the same quantile, compared to when all exposures are at their 50 th percentile.Plot B shows the independent associations of each exposure with SRS, with all other exposures at their 50th percentile.Plot C shows the association of a single exposure with SRS score, when a second exposure is set at varying quantiles, illustrating potential bivariate interactive effects.Plot D shows the association of a single exposure with SRS score as it increases from its 25 th to 75 th percentile while all other exposures are set at specific quantiles.For all plots, covariates are held constant.Results are adjusted for study site, maternal age, maternal race and ethnicity, maternal socioeconomic deprivation index, and child sex.Air pollutant values are reverse coded.Plot A shows the association of the overall mixture with ASD status, when all exposures are set at the same quantile, compared to when all exposures are at their 50 th percentile.Plot B shows the independent associations of each exposure with ASD status, with all other exposures at their 50th percentile.Plot C shows the association of a single exposure with ASD status, when a second exposure is set at varying quantiles, illustrating potential bivariate interactive effects.
Plot D shows the association of a single exposure with ASD status as it increases from its 25 th to 75 th percentile while all other exposures are set at specific quantiles.For all plots, covariates are held constant.

eFigure 2 .
Bayesian kernel machine regression (BKMR) results for the association of prenatal nutrient intake and trimester-specific air pollution exposures with child SRS total raw score in the EARLI study (n = 126) 14 EARLI -Early Autism Risk Longitudinal Investigation; SRS -Social Responsiveness Scale

eFigure 4 .
Probit Bayesian kernel machine regression (BKMR) results for the association of prenatal nutrient intake and air pollution exposures with child autism diagnosis in the EARLI study (n = 144) EARLI -Early Autism Risk Longitudinal Investigation eTable 1. Characteristics of EARLI participants overall (n=239) compared to those included in the analysis (n= 126) of nutrient intake, air pollution exposure, and autism outcomes Posterior inclusion probabilities (PIPs) for the association of prenatal nutrient intakes and air pollutant exposures with SRS score in the EARLI study, from Bayesian kernel machine regression models (n = 126) a Conditional PIPs describe the relative strength of the association with a unique exposure within a group.Adjusted models were adjusted for study site, maternal age, maternal race and ethnicity, maternal socioeconomic deprivation index, and child sex.eTable 4. Bivariate associations of prenatal nutrient intake and air pollution exposure with autism traits scores in the EARLI study (n = 126) a The association of average prenatal exposure to air pollutants with autism diagnosis in the EARLI study, stratified by nutrient intake below vs above the median a Bayesian kernel machine regression (BKMR) results for the association of prenatal nutrient intake and air pollution exposures with child SRS total raw score in the EARLI study, with further adjustment for covariates (n = 126) EARLI -Early Autism Risk Longitudinal Investigation; SRS -Social Responsiveness Scale DFE -Dietary Folate Equivalents; EARLI -Early Autism Risk Longitudinal Investigationa Does not include intake from supplements eTable 3. a PIPs are from Bayesian kernel machine regression analyses examining the association of prenatal nutrient intake and air pollution exposures with child SRS total raw score.PIPs describe the relative strength of the association of an exposure (or group of exposures) with SRS score.Individual PIPs describe the relative strength of associations with each unique exposure.Group PIPs describe the relative strength of the association with a group of exposures.aQuantileregression models were performed to examine the association of nutrient intake air pollutant exposures with SRS score at the 50 th percentile.Estimates are per 1 unit increase in nutrient or air pollutant exposures.Models were run separately for each nutrient and air pollutant.Adjusted models were adjusted for study site, maternal age, maternal race and ethnicity, maternal socioeconomic deprivation index, and child sex.bDoes not include intake from supplements eTable 5.